In this paper, we present a novel hybrid keyword spotting system that combines supervised and semi-supervised competitive learning algorithms. The first stage is a S-SOM (Semi-supervised Self- Organizing Map) module which is specifically designed for discrimination between keywords (KWs) and non-keywords (NKWs). The second stage is an FDVQ (Fuzzy Dynamic Vector Quantization) module which consists of discriminating between KWs detected by the first stage processing. The experiment on Switchboard database has show an improvement of about 6% on the accuracy of the system comparing to our best keyword-spotter one.